A proactive approach and forward-looking information to calculate reserves. Reserves are to be based on expected losses over the life of a loan.
SmartCECL helps to process historical data and prepares for the approval processes. It provides you various models including Probability of Default (PD) measures the percentage of loans that default. It can also estimate Loss Given Default (LGD), which is the amount of money a bank or other financial institution loses when a borrower defaults on a loan as well as Exposure at Default (EAD), which is the predicted amount of loss a bank may be exposed to when a debtor defaults on a loan.
SmartCECL provides a 360 degree view of Risk portfolio of lending including drill down views by products and customer groups and individual loans.
Our BigData platform helps you capture and store high volumes of granular loan level data and customer demographic information.
Our software will also reduce the risk of mathematical errors while still allowing proper control over the process. All this eliminates the room for human error, while helping your business run more efficiently with its existing personnel.
ALLL (Allowance for loan and lease losses) was based on "Incurred Loss" which was Backward-looking as it rely primarily on historical information. Credit losses were recognized only once they are considered “probable” and the losses could be estimated. This led to a 'Time lag' in how losses are reflected. Unfortunately, this issue was exposed only during the financial crisis, as this delay in loss recognition meant reserves were insufficient to cover institutions’ growing losses as credit deterioration accelerated.
Current Expected Credit Loss (CECL) standard is based on “life of loan” and makes sure that estimate of losses to be recorded for unimpaired loans at origination or purchase. It poses significant compliance and operational challenges for banks. CECL has its own roadmap, scenarios & modeling which are easy to adopt unlike ALLL.
CECL represents the most sweeping change and one of the most significant project for the banking industry ever. The 'life of loan' concept which CECL is based on presents the type of complexities that can decrease capital, add volatility to ALLL estimates as well as some additional costs. Needless to say, it has the potential to change how banks do business.
Our team of financial risk analysts, data scientists, decision scientists are working together for over 2.5 years studying market, understanding FASB requirements, identifying gaps in existing solutions and bridging those gaps.
We at Cognerium, are leveraging BigData and Cognitive methods to build highly scalable, flexible and robust solution that covers sixteen different loan types, thirty-two models, and more than ten reports.
As the CECL standard is already issued and to be effective soon, banks will need to start thinking strategically about its implication as well as start preparing for the implementation process as soon as possible. This is where our the cognitive smart solution called 'SmartCECL' can be of great help.
SmartCECL assists to process the data in a way CECL standard requires and by preparing the data for building various models like Probability of Default(PD), Loss Given Default(LGD) as well as Exposure at Default(EAD).
All of these will be useful to calculate the economic or regulatory capital for financial institutions. We can, therefore, ease the transition for your institution, and in fact, manage it end to end. The efficiency with which our software can process this data and calculate the information needed to make the credit decisions will save quite a few man-hours, in turn, saving your cost. SmartCECL can handle all of this within a fraction of time and with the minimal user input.